Mental Health Reform and Evolution of General Psychiatry in Ontario
Bibliographic record
Abstract
OBJECTIVES: To discuss developments in Ontario mental health reform, describe general psychiatric services in contrast to tertiary services, describe guidelines for the training of general psychiatrists, and suggest what changes may be required to develop an integrated mental health system (IMHS). METHOD: We review the Ontario government's recent blueprint for mental health reform and the Canadian federal government's document on best practices in psychiatry, in the context of defining general psychiatric services and their relation to tertiary services. From this, we consider the education of general psychiatrists and make suggestions for their training. RESULTS: General psychiatric services correspond to first-line and intensive psychiatric services delivered by community mental health agencies, community psychiatrists, and general hospitals for patients with moderate or serious mental illness. Many suggest that psychiatrists are not being trained to meet the needs of a reformed mental health system. An education program for general psychiatrists should include training in a wide range of community and general hospital settings, work within a multidisciplinary mental health team, and experience working in a shared care model with family physicians. CONCLUSIONS: Along with training general psychiatrists better, we must also develop recruitment and payment incentives, which would allow general psychiatrists who are based in the community and general hospitals to work within an IMHS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".